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library(data.table)
library(tidyr)
library(maps)
library(haven)
library(ggplot2)
library(dplyr)

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library(readxl)
hardship_complete <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/data/Hardship_complete.xlsx")
hardship_complete

NA
NA

hardship_complete$mean_homicide=log(hardship_complete$mean_homicide)
hardship_complete$gdp=log(hardship_complete$gdp)
hardship_complete$Infant_mortality=log(hardship_complete$Infant_mortality)
hardship_complete$life_expect=log(hardship_complete$life_expect)
hardship_complete$gini_income=log(hardship_complete$gini_income)
hardship_complete$`primary_ female_ enrollment_ rate`=log(hardship_complete$`primary_ female_ enrollment_ rate`)
hardship_complete

# Reverse Codierung
hardship_complete$mean_homicide=scale(hardship_complete$mean_homicide)
hardship_complete$gdp=scale(-hardship_complete$gdp)
hardship_complete$Infant_mortality=scale(hardship_complete$Infant_mortality)
hardship_complete$life_expect=scale(-hardship_complete$life_expect)
hardship_complete$gini_income=scale(hardship_complete$gini_income)
hardship_complete$gini_income=scale(-hardship_complete$`primary_ female_ enrollment_ rate`)
hardship_complete

hardship_complete$hardship_index=(hardship_complete$mean_homicide+hardship_complete$gdp+hardship_complete$gini_income+hardship_complete$life_expect+hardship_complete$Infant_mortality+hardship_complete$`primary_ female_ enrollment_ rate`)/6

hardship_complete

NA
NA

# Data of Wave 5


WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")


# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
WV5_data_df

#rename the variables
WV5_data <- WV5_data_df %>%
  rename(gender = V235, age = V237, country_code = V2, wave = V1, risktaking = V86, children = V56, married = V55, employed = V241, education = V238)
WV5_data

colnames(WV5_data)

  [1] "wave"          "V1A"           "V1B"           "country_code"  "V2A"           "V3"            "V4"            "V4_CO"         "V5"           
 [10] "V5_CO"         "V6"            "V6_CO"         "V7"            "V7_CO"         "V8"            "V8_CO"         "V9"            "V9_CO"        
 [19] "V10"           "V11"           "V12"           "V13"           "V14"           "V15"           "V16"           "V17"           "V18"          
 [28] "V19"           "V20"           "V21"           "V22"           "V23"           "V24"           "V25"           "V26"           "V27"          
 [37] "V28"           "V29"           "V30"           "V31"           "V32"           "V33"           "V34"           "V35"           "V36"          
 [46] "V37"           "V38"           "V39"           "V40"           "V41"           "V42"           "V43"           "V43_01"        "V43_02"       
 [55] "V43_03"        "V43_04"        "V43_05"        "V43_06"        "V43_07"        "V43_08"        "V43_09"        "V43_10"        "V43_11"       
 [64] "V43_12"        "V43_13"        "V43_14"        "V43_15"        "V43_16"        "V43_17"        "V43_18"        "V43_19"        "V43_20"       
 [73] "V43_21"        "V43_22"        "V43_23"        "V43_24"        "V43_25"        "V43_26"        "V43_27"        "V43_28"        "V43_29"       
 [82] "V43_30"        "V44"           "V45"           "V46"           "V47"           "V48"           "V49"           "V50"           "V51"          
 [91] "V52"           "V53"           "V54"           "married"       "children"      "V57"           "V58"           "V59"           "V60"          
[100] "V61"           "V62"           "V63"           "V64"           "V65"           "V66"           "V67"           "V68"           "V69"          
[109] "V69_HK"        "V70"           "V70_HK"        "V71"           "V72"           "V73"           "V73_HK"        "V74"           "V74_HK"       
[118] "V75"           "V76"           "V77"           "V78"           "V79"           "V80"           "V81"           "V82"           "V83"          
[127] "V84"           "V85"           "risktaking"    "V87"           "V88"           "V89"           "V90"           "V91"           "V92"          
[136] "V93"           "V94"           "V95"           "V96"           "V97"           "V98"           "V99"           "V100"          "V101"         
[145] "V102"          "V103"          "V104"          "V105"          "V106"          "V107"          "V108"          "V109"          "V110"         
[154] "V111"          "V112"          "V113"          "V114"          "V115"          "V116"          "V117"          "V118"          "V119"         
[163] "V120"          "V121"          "V122"          "V123"          "V124"          "V125"          "V126"          "V127"          "V128"         
[172] "V129"          "V130"          "V130_CA_1"     "V130_IQ_1"     "V130_IQ_2"     "V130_IQ_3"     "V130_IQ_4"     "V130_NZ_1"     "V130_NZ_2"    
[181] "V131"          "V132"          "V133"          "V134"          "V135"          "V136"          "V137"          "V138"          "V139"         
[190] "V140"          "V141"          "V142"          "V143"          "V144"          "V145"          "V146_00"       "V146_01"       "V146_02"      
[199] "V146_03"       "V146_04"       "V146_05"       "V146_06"       "V146_07"       "V146_08"       "V146_09"       "V146_10"       "V146_11"      
[208] "V146_12"       "V146_13"       "V146_14"       "V146_15"       "V146_16"       "V146_17"       "V146_18"       "V146_19"       "V146_20"      
[217] "V146_21"       "V146_22"       "V147"          "V148"          "V149"          "V150"          "V151"          "V151_IQ_A"     "V151_IQ_B"    
[226] "V152"          "V153"          "V154"          "V155"          "V156"          "V157"          "V158"          "V159"          "V160"         
[235] "V161"          "V162"          "V163"          "V164"          "V165"          "V166"          "V167"          "V168"          "V169"         
[244] "V170"          "V171"          "V172"          "V173"          "V174"          "V175"          "V176"          "V177"          "V178"         
[253] "V179"          "V180"          "V181"          "V182"          "V183"          "V184"          "V185"          "V186"          "V187"         
[262] "V188"          "V189"          "V190"          "V191"          "V192"          "V193"          "V194"          "V195"          "V196"         
[271] "V197"          "V198"          "V199"          "V200"          "V201"          "V202"          "V203"          "V204"          "V205"         
[280] "V206"          "V207"          "V208"          "V209"          "V210"          "V211"          "V212"          "V213A"         "V213B"        
[289] "V213C"         "V213D"         "V213E"         "V213F"         "V213G"         "V213H"         "V213K"         "V213L"         "V213M"        
[298] "V213N"         "V214"          "V215"          "V216"          "V217"          "V218"          "V219"          "V220"          "V221"         
[307] "V222"          "V223"          "V224"          "V225"          "V226"          "V227"          "V228"          "V229"          "V230"         
[316] "V231"          "V232"          "V233"          "V233A"         "V234"          "gender"        "V236"          "age"           "education"    
[325] "V238CS"        "V239"          "V240"          "employed"      "V242"          "V242A_CO"      "V243"          "V244"          "V245"         
[334] "V246"          "V247"          "V248"          "V249"          "V250"          "V251"          "V252"          "V252B"         "V253"         
[343] "V253CS"        "V254"          "V255"          "V255CS"        "V256"          "V257"          "V257B"         "V257C"         "V258"         
[352] "V259"          "V259A"         "V260"          "V261"          "V262"          "V263"          "V264"          "V265"          "S024"         
[361] "S025"          "Y001"          "Y002"          "Y003"          "SACSECVAL"     "SECVALWGT"     "RESEMAVAL"     "WEIGHTB"       "I_AUTHORITY"  
[370] "I_NATIONALISM" "I_DEVOUT"      "DEFIANCE"      "WEIGHT1A"      "I_RELIGIMP"    "I_RELIGBEL"    "I_RELIGPRAC"   "DISBELIEF"     "WEIGHT2A"     
[379] "I_NORM1"       "I_NORM2"       "I_NORM3"       "RELATIVISM"    "WEIGHT3A"      "I_TRUSTARMY"   "I_TRUSTPOLICE" "I_TRUSTCOURTS" "SCEPTICISM"   
[388] "WEIGHT4A"      "I_INDEP"       "I_IMAGIN"      "I_NONOBED"     "AUTONOMY"      "WEIGHT1B"      "I_WOMJOB"      "I_WOMPOL"      "I_WOMEDU"     
[397] "EQUALITY"      "WEIGHT2B"      "I_HOMOLIB"     "I_ABORTLIB"    "I_DIVORLIB"    "CHOICE"        "WEIGHT3B"      "I_VOICE1"      "I_VOICE2"     
[406] "I_VOI2_00"     "VOICE"         "WEIGHT4B"      "S001"          "S007"          "S018"          "S019"          "S021"          "COW"          

#select only the variables of interest
WV5_data <- WV5_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, married, employed, education)
WV5_data

# Read countrynames data from the CSV file (to decode the dataset 5)
countrynames <- read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header = FALSE, as.is = TRUE)
colnames(countrynames) <- c("code", "name")

# Assuming WV5_data has a column named country_code
WV5_data$country <- countrynames$name[match(WV5_data$country_code, countrynames$code)]

# Check the frequency of each country in the new column
table(WV5_data$country)

            Andorra           Argentina           Australia              Brazil            Bulgaria        Burkina Faso              Canada               Chile 
               1003                1002                1421                1500                1001                1534                2164                1000 
              China            Colombia          Cyprus (G)               Egypt            Ethiopia             Finland              France             Georgia 
               1991                3025                1050                3051                1500                1014                1001                1500 
            Germany               Ghana       Great Britain           Guatemala           Hong Kong             Hungary               India           Indonesia 
               2064                1534                1041                1000                1252                1007                2001                2015 
               Iran                Iraq               Italy               Japan              Jordan            Malaysia                Mali              Mexico 
               2667                2701                1012                1096                1200                1201                1534                1560 
            Moldova             Morocco         Netherlands         New Zealand              Norway                Peru              Poland             Romania 
               1046                1200                1050                 954                1025                1500                1000                1776 
             Russia              Rwanda            Slovenia        South Africa         South Korea               Spain              Sweden         Switzerland 
               2033                1507                1037                2988                1200                1200                1003                1241 
             Taiwan            Thailand Trinidad and Tobago              Turkey             Ukraine       United States             Uruguay            Viet Nam 
               1227                1534                1002                1346                1000                1249                1000                1495 
             Zambia 
               1500 

# Display the updated WV5_data
print(WV5_data)

#Read Dataset (Wave 6)

WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)

WV6_data <- WV6_data %>%
  rename(wave = V1, gender = V240, age = V242,country_code = V2, risktaking = V76, children = V58, married = V57, employed = V229, education = V248)


#select only the variables of interest

WV6_data <- WV6_data %>%
  dplyr::select(wave, gender, age, country_code,risktaking, children, married, employed, education)
WV6_data

countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country = countrynames$name [match(WV6_data$country_code, countrynames$code)]
table(WV6_data$country)

            Algeria           Argentina             Armenia           Australia          Azerbaijan             Belarus              Brazil               Chile 
               1200                1030                1100                1477                1002                1535                1486                1000 
              China            Colombia          Cyprus (G)             Ecuador               Egypt             Estonia             Georgia             Germany 
               2300                1512                1000                1202                1523                1533                1202                2046 
              Ghana               Haiti           Hong Kong               India                Iraq               Japan              Jordan          Kazakhstan 
               1552                1996                1000                4078                1200                2443                1200                1500 
             Kuwait          Kyrgyzstan             Lebanon               Libya            Malaysia              Mexico             Morocco         Netherlands 
               1303                1500                1200                2131                1300                2000                1200                1902 
        New Zealand             Nigeria            Pakistan           Palestine                Peru         Philippines              Poland               Qatar 
                841                1759                1200                1000                1210                1200                 966                1060 
            Romania              Russia              Rwanda           Singapore            Slovenia        South Africa         South Korea               Spain 
               1503                2500                1527                1972                1069                3531                1200                1189 
             Sweden              Taiwan            Thailand Trinidad and Tobago             Tunisia              Turkey             Ukraine       United States 
               1206                1238                1200                 999                1205                1605                1500                2232 
            Uruguay          Uzbekistan               Yemen            Zimbabwe 
               1000                1500                1000                1500 

WV6_data

range(WVS_data$age)

[1] 15 99

WVS_data = subset(WVS_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
data_Wave5 = subset(WV5_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
data_Wave6 = subset(WV6_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
WVS_data <- na.omit(WVS_data)
data_Wave5 <- na.omit(data_Wave5)
data_Wave6 <- na.omit(data_Wave6)


# Use the mutate function to change the country name
WVS_data <- WVS_data %>%
  mutate(country = ifelse(country == "Great Britain", "United Kingdom", country))

# Transfrom risk item such that high values represent more risk taking
WVS_data$risktaking = 6 - WVS_data$risktaking + 1

  
# Transform risk variable into T-score (mean = 50, sd = 10)
WVS_data$T_score_risktaking = 10*scale(WVS_data$risktaking, center=TRUE,scale=TRUE)+50

WVS_data

#Transform risk variable into Z score 

# Assuming T-scores have a mean of 50 and a standard deviation of 10
WVS_data$Z_score_risktaking = (WVS_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
print(WVS_data)

WVS_data <- WVS_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
WVS_data

WVS_data$gender = ifelse(WVS_data$gender == 1, 0, 1) # sex: male vs. female
WVS_data$children = ifelse(WVS_data$children == 0, 0, 1) # children: no vs. yes
WVS_data$married = ifelse(WVS_data$married == 1, 1, 0) # married: yes vs. no
WVS_data$employed = ifelse(WVS_data$employed < 4, 1, 0) # employed: yes vs. no
WVS_data$education = ifelse(WVS_data$education < 4, 0, 1) # education: no primary vs. primary+ 


hardship <- hardship_complete %>%
  dplyr::select(country, isocode, hardship_index)
hardship

WVS_mixed_model <- left_join(WVS_data, hardship, by = "country")
WVS_mixed_model

head(WVS_mixed_model)

library(lmerTest)

Attaching package: ‘lmerTest’

The following object is masked from ‘package:lme4’:

    lmer

The following object is masked from ‘package:stats’:

    step

# intercept only model
model0 = lmer(Z_score_risktaking ~ 1 + (1|country),data = WVS_mixed_model)
summary_model0=summary(model0)
summary_model0

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Z_score_risktaking ~ 1 + (1 | country)
   Data: WVS_mixed_model

REML criterion at convergence: 406605.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3179 -0.8254 -0.0722  0.7614  2.5318 

Random effects:
 Groups   Name        Variance Std.Dev.
 country  (Intercept) 0.08622  0.2936  
 Residual             0.90218  0.9498  
Number of obs: 148527, groups:  country, 76

Fixed effects:
            Estimate Std. Error       df t value Pr(>|t|)
(Intercept)  0.01243    0.03379 75.10025   0.368    0.714

# age, sex 
model1 = lmer(T_score_risktaking ~ 1 +scale(z_score_age)+factor(gender) + (1+scale(z_score_age)+factor(gender)|country),data = WVS_mixed_model)
summary_model1=summary(model1)
summary_model1

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: T_score_risktaking ~ 1 + scale(z_score_age) + factor(gender) +      (1 + scale(z_score_age) + factor(gender) | country)
   Data: WVS_mixed_model

REML criterion at convergence: 1081381

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4657 -0.7816 -0.0765  0.7536  3.2215 

Random effects:
 Groups   Name               Variance Std.Dev. Corr     
 country  (Intercept)         7.6729  2.7700            
          scale(z_score_age)  0.8133  0.9018   0.39     
          factor(gender)1     0.8723  0.9340   0.19 0.33
 Residual                    84.6041  9.1981            
Number of obs: 148527, groups:  country, 76

Fixed effects:
                   Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)         51.3360     0.3200 75.2652  160.42   <2e-16 ***
scale(z_score_age)  -1.9148     0.1068 74.8609  -17.93   <2e-16 ***
factor(gender)1     -2.3166     0.1193 72.3578  -19.43   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(__)
scl(z_scr_) 0.373        
fctr(gndr)1 0.135  0.287 

#model 2
library(lme4)

library(lme4)

# Define the lmer model and assign it to 'model_2'
model_2 <- lmer(T_score_risktaking ~ 1 + scale(z_score_age) + factor(gender) + factor(children) + 
                 factor(married) + factor(employed) + factor(education) + 
                 (1 + scale(z_score_age) + factor(gender) + factor(children) + 
                  factor(married) + factor(employed) + factor(education) | country),
                data = WVS_data)

# Display the summary of the model
summary(model_2)

anova(model0,model1)

refitting model(s) with ML (instead of REML)

Data: WVS_mixed_model
Models:
model0: Z_score_risktaking ~ 1 + (1 | country)
model1: T_score_risktaking ~ 1 + scale(z_score_age) + factor(gender) + (1 + scale(z_score_age) + factor(gender) | country)
       npar     AIC     BIC  logLik deviance Chisq Df Pr(>Chisq)
model0    3  406607  406637 -203300   406601                    
model1   10 1081396 1081495 -540688  1081376     0  7          1

anova(model1,model_2)

refitting model(s) with ML (instead of REML)

coefsallmodels=rbind(summary_model1$coefficients,
summary_model_2$coefficients,
summary_model_3$coefficients[c(1:2,4:8,3,9:10),])

write.csv(coefsallmodels,"coefsallmodels.csv")

# Read the CSV file into a data frame
gbd_mentalhealth <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/GBD_mentalhealth.xlsx")

gbd_mentalhealth

#select only the variables of interest
gbd_mentalhealth <- gbd_mentalhealth %>%
  dplyr::select(country, gender, age, cause, val, Measure)
gbd_mentalhealth

library(dplyr)

# Group data by country and age group, and calculate summary statistics
summary_by_country_age <- gbd_mentalhealth %>%
  group_by(country, age) %>%
  summarise(
    mean_DALYs = mean(val))  # Calculate mean of DALYs

`summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

library(dplyr)

# Assuming 'summary_by_country_age' contains your summarized dataset
mean_by_country <- summary_by_country_age %>%
  group_by(country) %>%
  summarise(mean_DALYs = mean(mean_DALYs))

# View the resulting mean by country
print(mean_by_country)

#log transform
mean_by_country$mean_DALYs=log(mean_by_country$mean_DALYs)
 
mean_by_country 

#Reverse codierung 
mean_by_country$mean_DALYs=scale(mean_by_country$mean_DALYs)

mean_by_country

#rename mean_DALYS
mental_health_index <- mean_by_country %>%
  rename('mental health' = mean_DALYs)
mental_health_index

NA

library(dplyr)

#######Anxiety disorders#########
#Filter data for a specific mental disorder (e.g., Anxiety Disorders)
anxiety_disorders <- gbd_mentalhealth %>%
  filter(cause == "Anxiety disorders")  # Change "Anxiety Disorders" to the desired disorder

# Calculate the mean of 'val' (Disability-Adjusted Life Years) across locations and ages
mean_DALYs <- mean(anxiety_disorders$val)

# Optionally, if you want to calculate mean by country:
anxiety_disorders <- anxiety_disorders %>%
  group_by(country) %>%
  summarise(mean_Anxiety_disorders = mean(val))

anxiety_disorders

#log transform
anxiety_disorders$mean_Anxiety_disorders=log(anxiety_disorders$mean_Anxiety_disorders)
 
anxiety_disorders

#Reverse codierung 
anxiety_disorders$mean_Anxiety_disorders=scale(anxiety_disorders$mean_Anxiety_disorders)
anxiety_disorders

mental_health_index

NA

gbd_mentalhealth

# Assuming gbd_mentalhealth is your data frame containing the 'cause' column

# Get unique values of 'cause' column
unique_causes <- unique(gbd_mentalhealth$cause)

# Create a data frame with unique causes
cause_table <- data.frame(Cause = unique_causes)

# Print the cause table
print(cause_table)

NA
NA

#######Bulimia nervosa########
library(dplyr)

# Filter the data for 'Bulimia Nervosa'
bulimia_nervosa <- gbd_mentalhealth %>%
  filter(cause == "Bulimia nervosa")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(bulimia_nervosa$val)

# Optionally, calculate mean by country
bulimia_nervosa <- bulimia_nervosa %>%
  group_by(country) %>%
  summarise(mean_bulimia_nervosa = mean(val))

# Log transform the mean_bulimia_nervosa column
bulimia_nervosa$mean_bulimia_nervosa <- log(bulimia_nervosa$mean_bulimia_nervosa)

# Scale the mean_bulimia_nervosa column (if needed)
bulimia_nervosa$mean_bulimia_nervosa <- scale(bulimia_nervosa$mean_bulimia_nervosa)

# Print the resulting data frame
print(bulimia_nervosa)

print(anxiety_disorders)

gbd_mentalhealth

NA

#####Attention-deficit/hyperactivity disorder
library(dplyr)


ADHD <- gbd_mentalhealth %>%
  filter(cause == "Attention-deficit/hyperactivity disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(ADHD$val)

# Optionally, calculate mean by country
ADHD <- ADHD %>%
  group_by(country) %>%
  summarise(mean_ADHD = mean(val))

# Log transform the mean_bulimia_nervosa column
ADHD$mean_ADHD <- log(ADHD$mean_ADHD)

# Scale the mean_bulimia_nervosa column (if needed)
ADHD$mean_ADHD <- scale(ADHD$mean_ADHD)

# Print the resulting data frame
print(ADHD)

NA

#########Idiopathic development intellectual ability 
library(dplyr)


Idiopathic_developmental_intellectual_disability <- gbd_mentalhealth %>%
  filter(cause == "Idiopathic developmental intellectual disability")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(Idiopathic_developmental_intellectual_disability$val)

# Optionally, calculate mean by country
Idiopathic_developmental_intellectual_disability <- Idiopathic_developmental_intellectual_disability %>%
  group_by(country) %>%
  summarise(mean_Idiopathic_developmental_intellectual_disability = mean(val))

# Log transform the mean_bulimia_nervosa column
Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability <- log(Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability)

# Scale the mean_bulimia_nervosa column (if needed)
Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability <- scale(Idiopathic_developmental_intellectual_disability$mean_Idiopathic_developmental_intellectual_disability)

# Print the resulting data frame
print(Idiopathic_developmental_intellectual_disability)

######Anorexia Nervosa

anorexia_nervosa <- gbd_mentalhealth %>%
  filter(cause == "Anorexia nervosa")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(anorexia_nervosa$val)

# Optionally, calculate mean by country
anorexia_nervosa <- anorexia_nervosa %>%
  group_by(country) %>%
  summarise(mean_anorexia_nervosa = mean(val))

# Log transform the mean_bulimia_nervosa column
anorexia_nervosa$mean_anorexia_nervosa <- log(anorexia_nervosa$mean_anorexia_nervosa)

# Scale the mean_bulimia_nervosa column (if needed)
anorexia_nervosa$mean_anorexia_nervosa <- scale(anorexia_nervosa$mean_anorexia_nervosa)

# Print the resulting data frame
print(anorexia_nervosa)

#####Depressive disorders#######

depressive_disorders <- gbd_mentalhealth %>%
  filter(cause == "Depressive disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(depressive_disorders$val)

# Optionally, calculate mean by country
depressive_disorders <- depressive_disorders %>%
  group_by(country) %>%
  summarise(mean_depressive_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
depressive_disorders$mean_depressive_disorders <- log(depressive_disorders$mean_depressive_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
depressive_disorders$mean_depressive_disorders <- scale(depressive_disorders$mean_depressive_disorders)

# Print the resulting data frame
print(depressive_disorders)

#######Autismus spectrum disorders######

autismus_spectrum_disorders <- gbd_mentalhealth %>%
  filter(cause == "Autism spectrum disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(autismus_spectrum_disorders$val)

# Optionally, calculate mean by country
autismus_spectrum_disorders <- autismus_spectrum_disorders %>%
  group_by(country) %>%
  summarise(mean_autismus_spectrum_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
autismus_spectrum_disorders$mean_autismus_spectrum_disorders <- log(autismus_spectrum_disorders$mean_autismus_spectrum_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
autismus_spectrum_disorders$mean_autismus_spectrum_disorders <- scale(autismus_spectrum_disorders$mean_autismus_spectrum_disorders)

# Print the resulting data frame
print(autismus_spectrum_disorders)

######Schizophrenia#######
schizophrenia <- gbd_mentalhealth %>%
  filter(cause == "Schizophrenia")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(schizophrenia$val)

# Optionally, calculate mean by country
schizophrenia <- schizophrenia %>%
  group_by(country) %>%
  summarise(mean_schizophrenia = mean(val))

# Log transform the mean_bulimia_nervosa column
schizophrenia$mean_schizophrenia <- log(schizophrenia$mean_schizophrenia)

# Scale the mean_bulimia_nervosa column (if needed)
schizophrenia$mean_schizophrenia <- scale(schizophrenia$mean_schizophrenia)

# Print the resulting data frame
print(schizophrenia)

NA

#######Conduct disorders#########
conduct_disorders <- gbd_mentalhealth %>%
  filter(cause == "Conduct disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(conduct_disorders$val)

# Optionally, calculate mean by country
conduct_disorders <- conduct_disorders %>%
  group_by(country) %>%
  summarise(mean_conduct_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
conduct_disorders$mean_conduct_disorders <- log(conduct_disorders$mean_conduct_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
conduct_disorders$mean_conduct_disorders <- scale(conduct_disorders$mean_conduct_disorders)

# Print the resulting data frame
print(conduct_disorders)

NA
NA

########Eating disorders#########
eating_disorders <- gbd_mentalhealth %>%
  filter(cause == "Eating disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(eating_disorders$val)

# Optionally, calculate mean by country
eating_disorders <- eating_disorders %>%
  group_by(country) %>%
  summarise(mean_eating_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
eating_disorders$mean_eating_disorders <- log(eating_disorders$mean_eating_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
eating_disorders$mean_eating_disorders <- scale(eating_disorders$mean_eating_disorders)

# Print the resulting data frame
print(eating_disorders)

NA

########Bipolar disorder#########
bipolar_disorder <- gbd_mentalhealth %>%
  filter(cause == "Bipolar disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(bipolar_disorder$val)

# Optionally, calculate mean by country
bipolar_disorder <- bipolar_disorder %>%
  group_by(country) %>%
  summarise(mean_bipolar_disorder = mean(val))

# Log transform the mean_bulimia_nervosa column
bipolar_disorder$mean_bipolar_disorder <- log(bipolar_disorder$mean_bipolar_disorder)

# Scale the mean_bulimia_nervosa column (if needed)
bipolar_disorder$mean_bipolar_disorder <- scale(bipolar_disorder$mean_bipolar_disorder)

# Print the resulting data frame
print(bipolar_disorder)

NA

print(cause_table)

########Substance use disorders########

substance_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Substance use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(substance_use_disorders$val)

# Optionally, calculate mean by country
substance_use_disorders <- substance_use_disorders %>%
  group_by(country) %>%
  summarise(mean_substance_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
substance_use_disorders$mean_substance_use_disorders <- log(substance_use_disorders$mean_substance_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
substance_use_disorders$mean_substance_use_disorders <- scale(substance_use_disorders$mean_substance_use_disorders)

# Print the resulting data frame
print(substance_use_disorders)

NA

####Drug use disorders

drug_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Drug use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(drug_use_disorders$val)

# Optionally, calculate mean by country
drug_use_disorders <- drug_use_disorders %>%
  group_by(country) %>%
  summarise(mean_drug_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
drug_use_disorders$mean_drug_use_disorders <- log(drug_use_disorders$mean_drug_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
drug_use_disorders$mean_drug_use_disorders <- scale(drug_use_disorders$mean_drug_use_disorders)

# Print the resulting data frame
print(drug_use_disorders)

NA
NA

##########Alcohol use disorders
alcohol_use_disorders <- gbd_mentalhealth %>%
  filter(cause == "Alcohol use disorders")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(alcohol_use_disorders$val)

# Optionally, calculate mean by country
alcohol_use_disorders <- alcohol_use_disorders %>%
  group_by(country) %>%
  summarise(mean_alcohol_use_disorders = mean(val))

# Log transform the mean_bulimia_nervosa column
alcohol_use_disorders$mean_alcohol_use_disorders <- log(alcohol_use_disorders$mean_alcohol_use_disorders)

# Scale the mean_bulimia_nervosa column (if needed)
alcohol_use_disorders$mean_alcohol_use_disorders <- scale(alcohol_use_disorders$mean_alcohol_use_disorders)

# Print the resulting data frame
print(alcohol_use_disorders)

NA

#######Major depressive disorders
major_depressive_disorder <- gbd_mentalhealth %>%
  filter(cause == "Major depressive disorder")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(major_depressive_disorder$val)

# Optionally, calculate mean by country
major_depressive_disorder <- major_depressive_disorder %>%
  group_by(country) %>%
  summarise(mean_major_depressive_disorder = mean(val))

# Log transform the mean_bulimia_nervosa column
major_depressive_disorder$mean_major_depressive_disorder <- log(major_depressive_disorder$mean_major_depressive_disorder)

# Scale the mean_bulimia_nervosa column (if needed)
major_depressive_disorder$mean_major_depressive_disorder <- scale(major_depressive_disorder$mean_major_depressive_disorder)

# Print the resulting data frame
print(major_depressive_disorder)

NA

#######Dysthymia########

dysthymia <- gbd_mentalhealth %>%
  filter(cause == "Dysthymia")

# Calculate the mean of 'val' (DALYs) across all locations
mean_DALYs <- mean(dysthymia$val)

# Optionally, calculate mean by country
dysthymia <- dysthymia %>%
  group_by(country) %>%
  summarise(mean_dysthymia = mean(val))

# Log transform the mean_bulimia_nervosa column
dysthymia$mean_dysthymia <- log(dysthymia$mean_dysthymia)

# Scale the mean_bulimia_nervosa column (if needed)
dysthymia$mean_dysthymia <- scale(dysthymia$mean_dysthymia)

# Print the resulting data frame
print(dysthymia)

NA
NA

library(dplyr)

# Perform left joins for each data frame on 'country'
final_mental_health_index <- mental_health_index %>%
  left_join(bulimia_nervosa, by = "country") %>%
  left_join(ADHD, by = "country") %>%
  left_join(Idiopathic_developmental_intellectual_disability, by = "country") %>%
  left_join(anorexia_nervosa, by = "country") %>%
  left_join(depressive_disorders, by = "country") %>%
  left_join(autismus_spectrum_disorders, by = "country") %>%
  left_join(conduct_disorders, by = "country") %>%
  left_join(schizophrenia, by = "country") %>%
  left_join(eating_disorders, by = "country") %>%
  left_join(bipolar_disorder, by = "country") %>%
  left_join(drug_use_disorders, by = "country") %>%
  left_join(alcohol_use_disorders, by = "country") %>%
  left_join(substance_use_disorders, by = "country") %>%
  left_join(major_depressive_disorder, by = "country") %>%
  left_join(dysthymia, by = "country")

# Display the resulting data frame
print(final_mental_health_index)

# Show the first few rows of the resulting data frame
head(final_mental_health_index)

NA

```